Terrestrial Laser Scanning (TLS) has been increasingly used in forestry applications including forest inventory and plant ecology. Tree biophysical properties such as leaf area distributions and wood volumes can be accurately estimated from TLS point clouds. In these applications, a prerequisite is to properly understand the information content of large scale point clouds (i.e., semantic labelling of point clouds), so that tree-scale attributes can be retrieved. Currently, this requirement is undergoing laborious and time consuming manual works. In this work, we jointly address the problems of semantic and instance segmentation of forest point clouds. Specifically, we propose an unsupervised pipeline based on a structure called superpoint graph, to simultaneously perform two tasks: single tree isolation and leaf-wood classification. The proposed method is free from restricted assumptions of forest types. Validation using simulated data resulted in a mean Intersection over Union (mIoU) of 0.81 for single tree isolation, and an overall accuracy of 87.7% for leaf-wood classification. The single tree isolation led to a relative root mean square error (RMSE%) of 2.9% and 19.8% for tree height and crown diameter estimations, respectively. Comparisons with existing methods on other benchmark datasets showed state-of-the-art results of our method on both single tree isolation and leaf-wood classification tasks. We provide the entire framework as an open-source tool with an end-user interface. This study closes the gap for using TLS point clouds to quantify tree-scale properties in large areas, where automatic interpretation of the information content of TLS point clouds remains a crucial challenge.

陆地激光扫描（TLS）已越来越多地用于包括林业清单和植物生态学在内的林业应用中。可以从TLS点云中准确估算树木的生物物理特性，例如叶面积分布和木材体积。在这些应用程序中，先决条件是正确理解大规模点云的信息内容（即，点云的语义标记），以便可以检索树级属性。当前，这一要求正在经历费力且耗时的手动工作。在这项工作中，我们共同解决了森林点云的语义和实例分割问题。具体来说，我们提出了一种基于称为超点图的结构的无监督管道，以同时执行两项任务：单树隔离和叶木分类。所提出的方法没有关于森林类型的限制性假设。使用模拟数据进行的验证得出，对于单棵树隔离而言，联合的平均交集（mIoU）为0.81，而叶木分类的总体准确度为87.7％。单树隔离导致树高和树冠直径估计的相对均方根误差（RMSE％）分别为2.9％和19.8％。与其他基准数据集上现有方法的比较显示了我们的方法在单树隔离和叶木分类任务上的最新结果。我们将整个框架作为带有最终用户界面的开源工具提供。这项研究弥补了使用TLS点云量化大面积树规模属性的空白，